Inference of 3D genome architecture by modeling overdispersion of Hi-C data - Bio-informatique (CBIO) Access content directly
Journal Articles Bioinformatics Year : 2023

Inference of 3D genome architecture by modeling overdispersion of Hi-C data


Abstract Motivation We address the challenge of inferring a consensus 3D model of genome architecture from Hi-C data. Existing approaches most often rely on a two-step algorithm: first, convert the contact counts into distances, then optimize an objective function akin to multidimensional scaling (MDS) to infer a 3D model. Other approaches use a maximum likelihood approach, modeling the contact counts between two loci as a Poisson random variable whose intensity is a decreasing function of the distance between them. However, a Poisson model of contact counts implies that the variance of the data is equal to the mean, a relationship that is often too restrictive to properly model count data. Results We first confirm the presence of overdispersion in several real Hi-C datasets, and we show that the overdispersion arises even in simulated datasets. We then propose a new model, called Pastis-NB, where we replace the Poisson model of contact counts by a negative binomial one, which is parametrized by a mean and a separate dispersion parameter. The dispersion parameter allows the variance to be adjusted independently from the mean, thus better modeling overdispersed data. We compare the results of Pastis-NB to those of several previously published algorithms, both MDS-based and statistical methods. We show that the negative binomial inference yields more accurate structures on simulated data, and more robust structures than other models across real Hi-C replicates and across different resolutions. Availability and implementation A Python implementation of Pastis-NB is available at under the BSD license. Supplementary information Supplementary data are available at Bioinformatics online.
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hal-04234569 , version 1 (10-10-2023)



Nelle Varoquaux, William Noble, Jean-Philippe Vert. Inference of 3D genome architecture by modeling overdispersion of Hi-C data. Bioinformatics, 2023, 39 (1), ⟨10.1093/bioinformatics/btac838⟩. ⟨hal-04234569⟩
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